| name | japanese-tech-writing |
|---|---|
| description | 日本語の技術文書・書籍原稿の文章規範。段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、翻訳調の比喩と擬人化の禁止（「運ぶ」「効く」「開かれた問い」など）、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。 |
| license | Unlicense（https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063） |
日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。
| from fastapi import FastAPI, HTTPException | |
| from shapely import GEOSException | |
| from matplotlib import pyplot as plt | |
| from fastapi.testclient import TestClient | |
| from shapely.geometry import Point, mapping | |
| import geopandas as gpd | |
| from geojson_pydantic import FeatureCollection |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Source: https://x.com/bot/status/2099890276315848743 (replay ~8:45:19)
Hosts: Matt Palmer (@mattyp), Lauren Tan (@poteto), Roshan Sadanani (@roshan_s)
Flag: Metrics/claims as stated on stream unless noted — not independently verified. Contest/prize fine print [unverified].
Builder pack consolidated from live scribe drops (player-time order). Prefer these timed bullets over inventing detail. BRB/empty stretches marked; gaps labeled when no notes were captured.
| -- autosave.lua | |
| -- | |
| -- Periodically saves "watch later" data during playback, rather than only saving on quit. | |
| -- This lets you easily recover your position in the case of an ungraceful shutdown of mpv (crash, power failure, etc.). | |
| -- | |
| -- You can configure the save period by creating a "lua-settings" directory inside your mpv configuration directory. | |
| -- Inside the "lua-settings" directory, create a file named "autosave.conf". | |
| -- The save period can be set like so: | |
| -- | |
| -- save_period=60 |
When creating an encrypted VM, VMware Workstation gives you the option to remember the password. It does this by storing the password in the Windows Credential Manager.
VMware does not provide a way to retrieve this stored password, but it can be accessed via the Win32 CredReadW API function.
There are a number of PowerShell projects including PowerShell Credential Manager which provide access to this API, but in testing I found they were unable to correctly display the VMware password.
This PowerShell example has been tested using Windows PowerShell (v5.1) and PowerShell (v7) using VMwa
I use pandoc to convert masses of Word documents to markdown. Still working on a generic script, but for now here's the "gist" of what I type into the terminal:
$ myfilename="example"
$ pandoc \
-t markdown_strict \
--extract-media='./attachments/$myfilename' \
$myfilename.docx \
| <# | |
| .SYNOPSIS | |
| Automatically detects and fixes WSL Interop naming conflict issue (WSLInterop-late vs WSLInterop) in Ubuntu-family WSL distributions. | |
| .DESCRIPTION | |
| This script identifies the systemd-binfmt vs wslu naming conflict in WSL Ubuntu-family distributions (Ubuntu 24.04+) | |
| and automatically installs a permanent systemd service fix. Only works with Ubuntu/Debian-based distributions that use apt and systemd. | |
| Issue: https://github.com/microsoft/WSL/issues/13449 | |
| // the cursor itself glides between cells instead of jumping. | |
| // Unlike a trail shader, this one is the focused cursor, so it | |
| // requires ghostty's own cursor to be hidden: | |
| // | |
| // cursor-opacity = 0 | |
| // custom-shader = "./shaders/cursor_glide.glsl" | |
| // --- CONFIGURATION --- | |
| const float DURATION = 0.14; // seconds for one glide |
